Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuine value by disclosing the three-way fit triage, that reasons are dated and sourced, that a 'need a check' state exists (partial/uncertain data), and that results are not AI-ranked. It does not explain the shopLocation vs destination interaction or result ordering/cost of a wide query.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the usage trigger, followed by return-shape and a useful 'No AI ranking' caveat; there is little filler. The middle sentence is long and packs criteria into a comma chain, which slightly reduces scannability but does not waste words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With annotations covering safety and an output schema covering return values, the remaining burden is usage and parameter meaning, and the description handles most of it. The notable gap is the unexplained relationship between destination (buyer) and shopLocation (seller), which affects eligibility results; pagination is also unaddressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 22%, so the description must compensate; it does so partially by mapping seller criteria to flags ('whether they need custom branding', 'want to start free', 'want to choose their print provider') and listing the product types. However, it only describes buyer location ('where buyers are') and leaves shopLocation's distinct meaning unclear, and it says nothing about limit/offset pagination over a potentially large result set.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource (suppliers) and the specific operation (matching suppliers to a seller's product type and shop constraints), and it spells out the return shape: three fit buckets plus a delivered price range. It never names a sibling tool (list_suppliers, get_supplier), so the boundary is inferred rather than stated, but the matching semantics are clear enough to separate it from the lookup tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit trigger ('Use this when a seller is choosing a supplier...') and enumerates the decision axes (buyer location, platform, branding, free start, provider choice) that make the tool appropriate. It stops short of exclusions or naming alternatives such as get_supplier_prices when the agent only needs pricing, so there is no when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.